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[quote=ābozmillar, post:9, topic:1599, full:trueā]
I think the main problem with plugins like these is that itās taking a backwards approach to āsmart plugins.ā I donāt think it makes sense to try to make an algorithm mix like a person, because thereās too much variability in how thatās done. [/quote]
Isnāt that what complex AI algorithms account for though? Think about how much variation there is in human speech, but voice recognition software manages to consolidate patterns of consonants and vowels into words and sentences based on the common thread between words in a similar language. I know that detecting speech is different than treating it, but couldnāt you take the same wave form analysis that youād run in Rosetta Stone or Amazon Alexa and use that to drive responses to a piece of software in a DAW?
Remember that last thread where AJ told me I needed to dump swatches of 400hz in a vocal? Why couldnāt a computer figure that out and do it for you? If he can hear it, surely an AI canā¦the question then isnāt hearing it. Its knowing what to do with itā¦right?
There may already be something like that out there, but Iād bet a lot of money that if someone made a filter that analyzed a body shape then ~removed~ a bra, itād quickly become the most pirated software on the planet.
Do you think that might just have to do with demand?
So the software thatās already out there can go head to head with any human, but only in areas of music where the rules are clearly defined. So these things can write inventions, fugues, toccatas, in the style of Bach, Handel, or Vivialdi based on the hundreds of works we have from them.
As for pop/rock/country/blues stuffā¦thereās already stuff on the market that can make suggestions. Hellā¦band in a box did that.
Alsoā¦for lyricsā¦thereās a case headed to the US supreme court over who would own the rights to lyrics that an AI generates. lol. My understanding of the case is that the software owner can claim them in terms of use agreements all day long, but the agreement may not mean anything at the end of the day.
Hell dudeā¦I suck at writing lyrics. I would love to have a computer spit out lyrics to a melody I write, then be able to sell and license them without having to pay a co-writer on the split sheet.
Why would it be good at that vs listening to a mishmash of frequencies then creating an EQ for it?
Whatās the difference? I meanā¦not with EZdrummer or EZkeys, but the difference between an AI and something that has a hard coded performance?
Fascinating thread, watching Jonathon talking to himself.
Itāll be fine, JK: someone WILL talk to you.
Well, I guess I now get the gracious honor of explaining how a forum works. We were talking on a previous thread about AI with Izotope. Boz started talking about AIās in general, so I copy pasted what @bozmillar said, with a response in the new thread. I sort of had to quote him there, because without his response, there wouldnāt be enough context to continue the discussion.
On this forum, when you see the little dot with someoneās logo, it means they responded.
Uhhā¦ok. Sure.
No JK, you donā;t need to explain forums. The primary poster on this thread is you. The blue stripe down the left of a post indictes it is a quote.
You have quoted yourself six times and answered YOURSELF five times.
Until I posted, no-one else had posted anything in this thread. Bozās reply comes from the original AI thread and YOU posted it as a quote.
Oh, and that little dot next to Bozās name. Is it or is it -n?
See, teaching grandad to suck eggs is a waste of time, especially when you are unable to understand the subject youāre explaining.
@Coquet-Shack, do you not have anything better to do? Maybe we need a separate section of the forum for you to post your irrelevant tirades.
Definitely a discussion I want to take part in, but responding to any of these questions would require both a lot of research and a lot of typing to do it any justice, both which I want to do. Iām out today, but I definitely want to talk about it more.
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Yeah i am interested in this line of thought as well. I am currently too busy to take the time to comment too heavily but i do think that the kind of thinking that boz is getting at. i like it when we can take the known and mix it (pun intended) and come up with some familiar and distinctly different. Anyway as i said no real time now but i will soon (I HOPE) and then i would love to read what others have to say about the subject.
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the only way i can see the program working and being able to mix for you is if it automatically compares the mix to hundreds of proof mixes in its memory banks. it would need to match genre, style, tempo, effects, etc all manor of things till it creates its own reference track made up from it taking bits from hundreds of other tracks. then it would match all the likes for likes and spit out a mix that sonicaly matches the bespoke reference . the programming for that would probably be larger than what first took man to the moon!
otherwise wouldnāt it just be working as an automated gate? filtering out set frequencies . i dont know. i`m pretty much out of my depth with this to be honest.
cool thread though Jonathan
:beerbang:
[quote=āLazyE, post:10, topic:1601, full:trueā]
the only way i can see the program working and being able to mix for you is if it automatically compares the mix to hundreds of proof mixes in its memory banks. it would need to match genre, style, tempo, effects, etc all manor of things till it creates its own reference track made up from it taking bits from hundreds of other tracks. [/quote]
Iād envision teaching this thing the same way you would any other AI. Dump several hundred reference mixes into its database then let it adapt. Just like the Amazon Alexa. You train it by teaching it to listen to something over and over again. And if it keeps tracks of what you change (after you make its suggestions), that data should be usable for improving the suggestions it makes. I realize that Amazon has a lot more money than Izotope or Waves, but weāre heading into an era where companies that create AI are poised to be the wealthiest entities on the planet. As AI technology becomes cheaper and more generic, why would the music industry not adopt it just like it did when Pro Tools first came out? And then dump it all the way down to freeware and our $70 Reaper licenses.
I donāt think it will stay like that forever. Unity and Unreal are freeware engines that allow wanna be game makers such as myself to design playable functional games having only studied about 3 months of coding. If a platform comes out like Unity, but for creating AIās instead of video games, the heavy lifting is taken care of by the engine, all the person has to do is learn the language the engine runs on. So in 5-10 short years from now, you or I could sitting around designing apps that learn shit all using a computer program that that is designed to make AIās accessible to dummies like me. You think?
well, neural networks donāt have a lot of code.But you do have to decide which features to send in to the NN be trained. The hard part is that itās really hard to know what features actually help the NN do a better job and which features make no difference. And there needs to be some sort of universally accepted outcome.
NNs can get stuck in ruts where they find the local maximum, but not the global maximum, which means it may discover a pathway that creates really great mixes for one set of tracks, or one style of tracks, but completely destroys a separate set of tracks
People fall into the same issues though. thatās where superstitions come from. When we attribute one thing to another because we find patterns in it, doesnāt mean the patterns actually exist. But our brains are far more complex than NN, and we can naturally break the patterns when we recognize there is an issue.
For example, itās completely possible for a NN to spit out white noise and think itās ok. It needs to receive the feedback telling it itās wrong in order for it to know.
Also, NN are much better at doing their thing when the results have a statistical element to it. For example, if you have a zillion pictures, and you want to use a NN to do facial recognition to find somebodyās face, it works really well. In google photos, I can enter my name and see a large list of pictures with my face in it from my personal library of pictures. It almost never gets it wrong. If thatās all you see, then you would think itās 100% accurate. But what you donāt see are all the pictures itās not showing me with my face on it because it go them wrong. Even if itās only 20% accurate, as long as the cutoff is set such that it only shows pictures of me that it is 95% possitive are me, then it looks to me like itās 100% accurate.
So while a NN can be really good and sorting through songs and picking out features that have good qualities, itās much harder to throw stuff at it and have it come back with 1 thing that is good every time.
Iām not saying itās not possible, Iām just saying that itās a far harder problem to solve than working the other way. Nobody wants a plugin that delivers really well 30% of the time but delivers pure junk 30% of the time.
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Hey man, Iām out of my depth too. The only reason this even crossed my mind is that I was sadly disappointed by Izotope Neutron. But heavily inspired by Mark Cubanās speech at Oxford on how automating automation is the future of technology.
Why isnāt it then just a matter of teaching it the difference between a country track and metal track?
When you disable the white noise plugin every time it tries, canāt the AI make note of that and not do it anymore?
So what if we backed off a bit and gave it an easier task. How about clip, isolate, and crop used vs unused regions in the daw. Then apply crossfades based on the type of instrument and speed of the transient. Then clean up unused regions from the audio pool or bin. Then detect the types of instruments used in the session. Then apply a picture graphic, a color scheme and a label to said instrument.
I mean, forget mixing stuff completely. If its really about sorting through features, canāt you at least have it clean up and organize a DAW session?
What is the difference between metal and country? I mean, you could use a NN to detect twang in a personās voice. But even if we were sticking to strict genres, does that really give you many hints on how to mix a song?
Mixing often requires decisions based on decisions based on decisions. You have to take into account all of the instruments used and their purpose for being used. I think it would be really hard for a NN to hear a song and say āThis song would really benefit from the piano driving the chorus, but not the verse.ā
And in order to train it, something has to tell it whether it did it right or wrong. Itās hard enough for Pandora to know what kind of music I like because the act of training it is very binary. I have to tell it that I either like a song or I donāt. Often times, I canāt quantify why I donāt like a song.
Iām not talking about a white noise plugin. Iām talking about the fact that NNs process things in very unpredictable ways. You can get some hints about what features might have a significant impact on the NNās ability to give you good results. They are really really good for some tasks, and really really bad for other tasks. And for some tasks, they could be really good if we could provide it with enough data. Add to the fact that thereās no real agreed upon end point, how do you even go about training it?
That seems like a better job for hard coding that NN. Mixing in itās current form is more of a game of setting things up in a way that you are comfortable. I already have actions in reaper that do this for me, but they only make sense to me. Doing something that is useful for lots of people would be really hard, because everyone has different requirements. How do you train something when everybody wants it trained differently?
Again, Iām not saying it canāt be done. But I do think there are better and easier ways to utilize AI in music production.
Just to be clear, is there a difference between neural networking and machine learning?
no, NN is a common method of doing machine learning, but there are other methods as well.
So I see 3 different problems? Making distinctions, making correct decisions and prioritizing processes based on an āintuitionā, and then arriving at en end result is far more complicated than Netflix figuring out which shows you probably wonāt want to watch.
I think Iām starting to see where my own misconceptions are confusing the issue. It has to do with most machine learning being focused on arriving at a specific concrete solution, and under more controlled circumstances, with far less variables to account for.
Machine learning is basically really good at pattern recognition. And not just obvious patterns, but patterns that may not look like patterns. Basically any patterns that will help it arrive at the correct outcome the most often.
For example, it would be really really hard to program code that could pick out a telephone booth in a picture. Telephone booths come in all sorts of shapes and sizes, but under normal circumstances, a person can look at an object and know whether or not itās a telephone booth intuitively. Itās not something you need to think about. You just know it because you know it.
NN are the same way. You can train it to know what a telephone booth looks like and it just knows it. Then you can give it a picture of a random telephone booth and it will know that it is one. That kind of thing just couldnāt be done with brute force programming. And there are many similar problems like that that brute force programming just couldnāt do in the past. It really opens up a lot of possibilities with new things that can be done.
But itās still not easy. You canāt just give it raw data and expect it to work well. Too much data and it might find irrelevant patterns. Too much data and it might not be able to find any meaningful patterns. So you really have to spoon feed it the right information so that it can process it reliably. And that information often doesnāt make that much sense to the programmer.
Also, keep in mind that we live in a world where if something is failing 1% of the time, it drives us nuts. We expect high levels of reliability. It would take a ton of research and testing to be able to come up with something that worked reliably.
No matter how good a programmer or how good a program it will never be able for o make artistic decisions based on what sounds nice. Its just crunching data. Soulless
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I wasnāt assuming it would make artistic decisions. I had merely hoped (Izotope Neutron in particular) would be able to make surgical corrective ones based on frequency analysis, which at first I thought would be comparable to āsoulless dataā.